Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data

Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.

Variations in the Spatial Distribution of Smart Parcel Lockers in the Central Metropolitan Region of Tianjin, China: A Comparative Analysis before and after COVID-19

The COVID-19 pandemic has led to a significant increase in e-commerce, which has prompted residents to shift their purchasing habits from offline to online. As a result, Smart Parcel Lockers (SPLs) have emerged as an accessible end-to-end delivery service that fits into the pandemic strategy of maintaining social distance and no-contact protocols. Although numerous studies have examined SPLs from various perspectives, few have analyzed their spatial distribution from an urban planning perspective, which could enhance the development of other disciplines in this field. To address this gap, we investigate the distribution of SPLs in Tianjin's central urban area before and after the pandemic (i.e., 2019 and 2022) using kernel density estimation, average nearest neighbor analysis, standard deviation elliptic, and geographical detector. Our results show that, in three years, the number of SPLs has increased from 51 to 479, and a majority were installed in residential communities (i.e., 92.2% in 2019, and 97.7% in 2022). We find that SPLs were distributed randomly before the pandemic, but after the pandemic, SPLs agglomerated and followed Tianjin's development pattern. We identify eight influential factors on the spatial distribution of SPLs and discuss their individual and compound effects. Our discussion highlights potential spatial distribution analysis, such as dynamic layout planning, to improve the allocation of SPLs in city planning and city logistics.

Machine learning approach for estimating the human-related VOC emissions in a university classroom.

Indoor air quality becomes increasingly important, partly because the COVID-19 pandemic increases the time people spend indoors. Research into the prediction of indoor volatile organic compounds (VOCs) is traditionally confined to building materials and furniture. Relatively little research focuses on estimation of human-related VOCs, which have been shown to contribute significantly to indoor air quality, especially in densely-occupied environments. This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom. The time-resolved concentrations of two typical human-related (ozone-related) VOCs in the classroom over a five-day period were analyzed, i.e., 6-methyl-5-hepten-2-one (6-MHO), 4-oxopentanal (4-OPA). By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression (RFR), adaptive boosting (Adaboost), gradient boosting regression tree (GBRT), extreme gradient boosting (XGboost), and least squares support vector machine (LSSVM), we find that the LSSVM approach achieves the best performance, by using multi-feature parameters (number of occupants, ozone concentration, temperature, relative humidity) as the input. The LSSVM approach is then used to predict the 4-OPA concentration, with mean absolute percentage error (MAPE) less than 5%, indicating high accuracy. By combining the LSSVM with a kernel density estimation (KDE) method, we further establish an interval prediction model, which can provide uncertainty information and viable option for decision-makers. The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors, making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings.

Local Naïve Bayes for Predicting Evolution of COVID-19 Patients on Self Organizing Maps

The most recent Clinical Decision Support Systems use the potential of Machine Learning techniques to target clinical problems, avoiding the use of explicit rules. In this paper, a model to monitor and predict the risk of unfavourable evolution (UE) during hospitalization of COVID-19 patients is proposed. It combines Self Organizing Maps and local Naïve Bayes (NB) classifiers because of interpretation purposes. We used the results of six blood tests (leukocytes, D-dimer, among others) provided by a Spanish hospital group. The probabilistic approach allows us to get the daily risk of UE for each patient in an interpretable way. Several variants of the NB classifiers family have been explored, mainly weighting and likelihood estimation (parametric and nonparametric). Despite the over-simplified assumptions of the NB classifiers, they provided good predictive results in terms of sensitivity and specificity. The model with nonparametric likelihood estimation provided the best risk prediction over time even when designed with a limited number of samples. Specifically, the median value and interquartil range for the risk prediction were quite reliable even 10 days before the event day for patients hospitalized longer than 7 days. The risk median values also agree with the gold-standard for patients with a hospital stay shorter than 7 days, though the interquartil range can be too wide (probably because of the variability in the inpatient days - sometimes, just 2 days). Though a deepest analysis considering more patients and features would be convenient, our results show the potential of the proposed approach, both from a technical and clinical viewpoint. © 2022 IEEE.

Autoencoder-based Unsupervised Anomaly Detection for Covid-19 Screening on Chest X-Ray Images

The COVID-19 outbreak is a major global catastrophe of our time and the largest hurdle since World War II. According to WHO, as of July 2022, there are more than 571 million confirmed cases of COVID-19 and over six million deaths. The issue of identifying unexpected inputs based on trained examples of normal data is known as anomaly detection. In the case of diagnosing covid-19, Chest X-ray disorders that are hardly apparent are extremely challenging to identify. Although various well-known supervised classification methods are being applied for that purpose, however in the real scenario, healthy patients' data is tremendously available but contaminated samples are scarce. The process of gathering samples from ill patients is troublesome and takes a lengthy time. To address the issue of data imbalance in anomaly detection, this research demonstrates an unsupervised learning technique using a convolutional autoencoder in which the training phase does not include any infected sample. Being trained only with the healthy data, The patterns of the healthy samples are preserved in latent vector space and can differentiate ill samples by observing substantial divergence from the distribution of healthy data. Higher reconstruction error and lower KDE (Kernel Density Estimation) indicate affected data. By contrasting the reconstruction error and KDE of healthy data with anomalous data, the suggested technique is feasible for identifying anomalous samples. © 2022 IEEE.

A Generalized Entropy Approach to Portfolio Selection under a Hidden Markov Model

This paper develops a dynamic portfolio selection model incorporating economic uncertainty for business cycles. It is assumed that the financial market at each point in time is defined by a hidden Markov model, which is characterized by the overall equity market returns and volatility. The risk associated with investment decisions is measured by the exponential Rényi entropy criterion, which summarizes the uncertainty in portfolio returns. Assuming asset returns are projected by a regime-switching regression model on the two market risk factors, we develop an entropy-based dynamic portfolio selection model constrained with the wealth surplus being greater than or equal to the shortfall over a target and the probability of shortfall being less than or equal to a specified level. In the empirical analysis, we use the select sector ETFs to test the asset pricing model and examine the portfolio performance. Weekly financial data from 31 December 1998 to 30 December 2018 is employed for the estimation of the hidden Markov model including the asset return parameters, while the out-of-sample period from 3 January 2019 to 30 April 2022 is used for portfolio performance testing. It is found that, under both the empirical Sharpe and return to entropy ratios, the dynamic portfolio under the proposed strategy is much improved in contrast with mean variance models.

A Clustering Mechanism to Identify Close Contact for the Ship Passenger Health

COVID-19 is spreading globally, and this spread is continuous. Ships have become the leading platform for virus transmission as a means of transportation. The small space of ships makes the possibility of virus outbreaks highly increased. The current way to effectively interrupt the spread of the virus is to track close contacts and physically isolate them. Therefore, the identification of close contacts becomes critical. This paper proposes a close contact identification algorithm applicable to the ship environment. The user ID is creatively proposed as the initialized location point cluster in this algorithm. And the KDE is introduced into the clustering process of the algorithm, and the center of the cluster is calculated by using the KDE of the location points as weights. The threshold value is used as the criterion for merging the clusters. Finally, the correct cluster result is obtained. This algorithm can provide technical support for ship companies to sustainably manage ships in the post-epidemic era, thus serving the purpose of maximizing the protection of ship passengers' health. © 2022 IEEE.

Tracing out the effect of transportation infrastructure on NO2 concentration levels with Kernel Density Estimation by investigating successive COVID-19-induced lockdowns.

This study aims to investigate the effect of transportation infrastructure on the decrease of NO2 air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO2 air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot-coldspot analysis on the relative change in NO2 air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO2 air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R2 = 0.808). The results showed that the largest decrease in NO2 air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = -0.904, R2 = 0.818). Economic and population predictors also explained with good fit the decrease in NO2 air pollution during the first lockdown: GDP (R2 = 0.511), employees (R2 = 0.513), population density (R2 = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.

Exploiting probability density function of deep convolutional autoencoders' latent space for reliable COVID-19 detection on CT scans.

We present a probabilistic method for classifying chest computed tomography (CT) scans into COVID-19 and non-COVID-19. To this end, we design and train, in an unsupervised manner, a deep convolutional autoencoder (DCAE) on a selected training data set, which is composed only of COVID-19 CT scans. Once the model is trained, the encoder can generate the compact hidden representation (the hidden feature vectors) of the training data set. Afterwards, we exploit the obtained hidden representation to build up the target probability density function (PDF) of the training data set by means of kernel density estimation (KDE). Subsequently, in the test phase, we feed a test CT into the trained encoder to produce the corresponding hidden feature vector, and then, we utilise the target PDF to compute the corresponding PDF value of the test image. Finally, this obtained value is compared to a threshold to assign the COVID-19 label or non-COVID-19 to the test image. We numerically check our approach's performance (i.e. test accuracy and training times) by comparing it with those of some state-of-the-art methods.

Spatial and Temporal Analysis of COVID-19 in the Elderly Living in Residential Care Homes in Portugal.

BACKGROUND: The goal of this study is to identify geographic areas for priority actions in order to control COVID-19 among the elderly living in Residential Care Homes (RCH). We also describe the evolution of COVID-19 in RHC throughout the 278 municipalities of continental Portugal between March and December 2020. METHODS: A spatial population analysis of positive COVID-19 cases reported by the Portuguese National Health Service (NHS) among the elderly living in RCH. The data are for COVID-19 testing, symptomatic status, comorbidities, and income level by municipalities. COVID-19 measures at the municipality level are the proportion of positive cases of elderly living in RCH, positive cases per elderly living in RCH, symptomatic to asymptomatic ratio, and the share of comorbidities cases. Spatial analysis used the Kernel density estimation (KDE), space-time statistic Scan, and geographic weighted regression (GWR) to detect and analyze clusters of infected elderly. RESULTS: Between 3 March and 31 December 2020, the high-risk primary cluster was located in the regions of Braganca, Guarda, Vila Real, and Viseu, in the Northwest of Portugal (relative risk = 3.67), between 30 September and 13 December 2020. The priority geographic areas for attention and intervention for elderly living in care homes are the regions in the Northeast of Portugal, and around the large cities, Lisbon and Porto, which had high risk clusters. The relative risk of infection was spatially not stationary and generally positively affected by both comorbidities and low-income. CONCLUSION: The regions with a population with high comorbidities and low income are a priority for action in order to control COVID-19 in the elderly living in RCH. The results suggest improving both income and health levels in the southwest of Portugal, in the environs of large cities, such as Lisbon and Porto, and in the northwest of Portugal to mitigate the spread of COVID-19.

On semiparametric inference for periodically modulated density functions

We consider semiparametric inference for seasonally modulated density functions. Asymptotic results for kernel based estimators and simultaneous confidence bands are derived. The method is illustrated by an analysis of COVID-19 data from six European countries. [ FROM AUTHOR] Copyright of Communications in Statistics: Theory & Methods is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

Over the last three decades, traffic crashes have been one of the leading causes of fatalities and economic losses in the U.S.;compared with other age groups, this is especially concerning for the youth population (those aged between 16 and 24), mostly due to their inexperience, greater inattentiveness, and riskier behavior while driving. This research intends to investigate this issue around selected Florida university campuses. We employed three methods: (1) a comparative assessment for three selected counties using both planar Euclidean Distance and Roadway Network Distance-based Kernel Density Estimation methods to determine high-risk crash locations, (2) a crash density ratio difference approach to compare the maxima-normalized crash densities for the youth population and those victims that are 25 and up, and (3) a logistic regression approach to identify the statistically significant factors contributing to young-driver-involved crashes. The developed GIS maps illustrate the difference in spatial patterns of young-driver crash densities compared to those for other age groups. The statistical findings also reveal that intersections around university areas appear to be significantly problematic for youth populations, regardless of the differences in the general perspective of the characteristics of the selected counties. Moreover, the speed limit countermeasures around universities could not effectively prevent young-driver crash occurrences. Hence, the results of this study can provide valuable insights to transportation agencies in terms of pinpointing the high-risk locations around universities, assessing the effectiveness of existing safety countermeasures, and developing more reliable plans with a focus on the youth population.

Investigating spatiotemporal indoor contact patterns using ABM and STKDE

The transmission of infectious disease, including SARS-CoV-2, mainly takes place indoors through close contact of individuals. The federal government issued the social distancing guideline of 6 feet to curb the spread. However, such static guidelines might fail to decrease contacts since they ignore space-dependent pedestrian movement. Therefore, we built an agent-based model (ABM) to investigate contact patterns when people enter a large venue, such as a sports arena. In addition, we explore the spatiotemporal contact patterns using space-time kernel density estimation (STKDE). Our results indicate a significant reduction of contacts when controlling the entrance time intervals (time between individual entrances to the venue). In addition, many contacts occur between individuals who move inside the seating section to seek their own seat and those who already sit. This implies we need to find additional solutions to reduce contacts in the seating section in the future study. This research could help decision makers to form detailed social distancing requirements, especially depending on the spatial structure of large indoor spaces. © 2021 ACM.

Toward Combatting COVID-19: A Risk Assessment System.

The coronavirus disease 2019 (COVID-19) has rapidly become a significant public health emergency all over the world since it was first identified in Wuhan, China, in December 2019. Until today, massive disease-related data have been collected, both manually and through the Internet of Medical Things (IoMT), which can be potentially used to analyze the spread of the disease. On the other hand, with the help of IoMT, the analysis results of the current status of COVID-19 can be delivered to people in real time to enable situational awareness, which may help mitigate the disease spread in communities. However, current accessible data on COVID-19 are mostly at a macrolevel, such as for each state, county, or metropolitan area. For fine-grained areas, such as for each city, community, or geographical coordinate, COVID-19 data are usually not available, which prevents us from obtaining information on the disease spread in closer neighborhoods around us. To address this problem, in this article, we propose a two-level risk assessment system. In particular, we define a "risk index." Then, we develop a risk assessment model, called MK-DNN, by taking advantage of the multikernel density estimation (MKDE) and deep neural network (DNN). We train MK-DNN at the macrolevel (for each metro area), which subsequently enables us to obtain the risk indices at the microlevel (for each geographic coordinate). Moreover, a heuristic validation method is further designed to help validate the obtained microlevel risk indices. Simulations conducted on real-world data demonstrate the accuracy and validity of our proposed risk assessment system.

The spatio-temporal distribution of COVID-19 infection in England between January and June 2020.

The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed.A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk.Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout.In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm.

In this study, the Intelligent Infectious Diseases Algorithm (IIDA) has been developed to locate the sources of infection and survival rate of coronavirus disease 2019 (COVID-19), in order to propose health care routes for population affected by COVID-19. The main goal of this computational algorithm is to reduce the spread of the virus and decrease the number of infected people. To do so, health care routes are generated according to the priority of certain population groups. The algorithm was applied to New York state data. Based on infection rates and reported deaths, hot spots were determined by applying the kernel density estimation (KDE) to the groups that have been previously obtained using a clustering algorithm together with the elbow method. For each cluster, the survival rate -the key information to prioritize medical care- was determined using the proportional hazards model. Finally, ant colony optimization (ACO) and the traveling salesman problem (TSP) optimization algorithms were applied to identify the optimal route to the closest hospital. The results obtained efficiently covered the points with the highest concentration of COVID-19 cases. In this way, its spread can be prevented and health resources optimized.